Multiview clustering via nonnegative matrix factorization based on graph agreement

Abstract

With the rapid development of information technology, one object can usually be described by multiple views. Intuitively, the diversity and complementarity of different views provide a more comprehensive data description, which can lead to better performance on multiview clustering tasks. Motivated by the fact that how to fully discover diversity and complementary information across views is the key point to deal with multiview clustering, we propose a multiview learning method, termed as graph-agreement nonnegative matrix factorization (GANMF). GANMF attempts to implement this goal based on matrix factorization technology while exploiting the rich multiview information based on the intra- and interview aspects. Specifically, the graph agreement between representation space and raw space is maximized to preserve the intrinsic geometric property in individual view for the intraview case. Similarly, the graph structures in different views are expected to keep consistent with each other by minimizing the divergence between pairwise views. To this end, the intrinsic information and geometric structure information in the intraview case and complementary and compatibility information in the interview case can be simultaneously formulated into one framework. To solve the proposed GANMF, we correspondingly develop an effective algorithm based on iterative alternating strategy. Extensive experimental results on seven multiview datasets demonstrate the superiority and effectiveness of our proposed method.